Fast auralization using radial basis functions type of artificial neural network techniques

Abstract

This work presents a new technique to produce fast and reliable auralizations with a computer code for room acoustics simulation. It discusses the binaural room impulse responses generation classic method and presents a new technique using radial basis functions type of artificial neural networks. The radial basis functions type of artificial neural networks is briefly presented and its training and testing procedures are discussed. The artificial neural network models the filtered head-related impulse responses for 64,442 directions uniformly distributed around the head with a significant reduction in computational cost of around 90% in the generation of binaural impulse responses. It is shown that the filtered head-related impulse responses calculated with the classical convolution method and with the artificial neural network technique are almost indistinguishable. It is concluded that the new technique produces fastest and reliable binaural room impulse responses for auralization purposes.